Spark & Big Data questions from Capgemini data engineering interviews.
These spark & big data questions are sourced from Capgemini data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (8 of 13 are tagged hard). Recurring themes are spark, optimization, and partition — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Infosys, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 13 curated questions: 4 easy, 1 medium, and 8 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are spark (12), optimization (7), partition (6), sql (4), join (2), and python (2). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
What is the purpose of the Bronze, Silver, and Gold layers in a data pipeline?
Adaptive Query Execution (AQE): Discuss how AQE optimizes query execution in Spark dynamically based on runtime stats.
Cache() vs Persist(): Explain the difference and use cases for caching and persisting data in Spark with memory levels.
Define what a User-Defined Function (UDF) is and how to register it in PySpark.
Describe the cluster configuration used in your project, including memory allocation, number of nodes, and executor/driver settings.
Discuss how you integrated Azure services into your Spark application.
Discuss the process of moving files in Databricks File System (DBFS).
Explain the architecture of Spark, including its components such as driver, executor, and cluster manager.
List all the technologies you have worked on in your project (e.g., Spark, Hadoop, Hive, Databricks).
Solve the dataset transformation using PySpark.
Solve the grade assignment problem using a UDF in PySpark.
What performance optimization techniques have you applied in Spark, Sqoop, or Databricks?
Which Spark version are you using in your project, and why did you choose it?
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